Abstract

This study presents a novel financial performance forecasting method that combines the threshold technique with Artificial Neural Networks (ANN). It applies the threshold regression method to identify the factors within the board of directors that influence the financial performance of traditional industries in Taiwan. The findings indicate that the ANN method effectively predicts financial performance by using relevant board structure data. Furthermore, the empirical results suggest that boards with more members demonstrate increased profitability. Additionally, a more significant presence of board members with accounting expertise contributes to more consistent profits. In contrast, an increased presence of members with financial expertise has a more pronounced impact on profitability.

Introduction

Traditional industries, including sectors such as metal machinery, the chemical industry, and subsistence industries, have played pivotal roles in Taiwan’s economic development, trade, and industrial evolution over the years. However, in recent years we have witnessed the emergence of economic powerhouses and the rapid progress of regional economic and trade partnerships like ASEAN, resulting in significant changes to the global economic and trade landscape. As a result, these industries are now operating within a new and highly competitive environment. In this era of rapid transformation, these industries must identify the essential factors that will enable them to maintain and enhance their positions on the international stage.

Investors seek to achieve multiple objectives through the election of a company’s board of directors. They aim to enhance corporate governance, safeguard the rights and interests of shareholders, and anticipate that the board will contribute expertise to improve the decision-making prowess of the board itself. Ultimately, this is expected to enhance the company’s overall performance. Consequently, whether the board of directors can positively impact corporate performance has remained a significant research focus in management and academia.

While many studies have explored the relationship between the board of directors’ characteristics and company performance, a notable need exists for more in-depth articles examining the professional backgrounds of the board members and CEOs and how their leadership qualities influence financial performance. This gap in research underscores the need for a more comprehensive understanding of the intricate dynamics between leadership, professional expertise, and corporate financial outcomes.

Conclusions

The research results show that, for companies in traditional industries that are listed on the Taiwanese stock market, the composition of the board of directors impacts the business’s financial performance.

Boards with more members may have a greater capacity for overseeing corporate governance. A greater representation of board members with accounting expertise tends to lead to more consistent corporate profits, whereas an increased presence of members with financial expertise results in more pronounced profit fluctuations. Additionally, in the case of companies with lower profits, managers may face pressure from loan contracts or receive limited attention from investors, potentially leading to heightened practices related to profit management. Managers may aim to maximize their benefits. At this time, the return on assets is high, and the managers obtain the expected benefits. Thus, to restrain the profit management behavior of managers, enterprises need to have a broader view and have specific judgments and analyses in each enterprise to build organizational structure and procedures. The operation of the board of directors is more effective because of the industry’s characteristics, the enterprise’s size, the capital structure, and the performance of the enterprise. Finally, this study used deep learning and big data techniques to build an Artificial Neural Network (ANN) model that specializes in predicting the ROA of companies based on most factors related to board structure, with a prediction accuracy of over 78%.

Abstract

     Today, industry 4.0 is becoming a major target for cybercriminals due to its hyper-connectivity. Fortunately, there are several advanced means of securing industrial systems such as Intrusion Detection Systems (IDS). However, one of the main limitations of industrial IDS is the high rate of false positives and how to distinguish a real attack from an industrial failure. This paper deals precisely with the two latter points and proposes a way to reduce the rate of false positives and to distinguish attacks from industrial failures. The proposed approach combines two kinds of IDS using Neural Network (NN) through a Decision Making System (DMS). It was tested on a real industrial environment. The performance results are promising with a high percentage of accuracy and a low false positive rate.

Introduction

     Nowadays, Industrial Control Systems (ICS) exist in many different industrial sectors such as meatpacking, chemistry, construction, automotive, electronics industry. But also in vital industrial sectors such as energy, health, military and food sectors. Therefore, the suspension or the stopping of these systems could be costly for industrialists and cause consequent damage. Today, securing such equipment becomes more than necessary. Over the past decade, industry has become the center of attackers’ focus and has been the victim of several attacks starting with Stuxnet, Black Energy, WannaCry. This wave of attacks has been succeeded by several ransomware attacks in 2020 during the coronavirus pandemic, especially with the increase in the number of remote workers and a lack of security in this new working model [1]. Kaspersky’s ICS CERT researchers forecast a list of attacks likely to target industries in 2023 [2]. Among these attacks are phishing pages and emails, Torjans, N-day vulnerabilities, attacks on cloud services, exploiting vulnerabilities in legitimate software, the spread of malware via removable media …

This cyber-criminality phenomenon is favored with the emergence of the industry 4.0. This 4th industrial revolution is characterized by the convergence of the worlds of Information Technology (IT) and Operation Technology (OT), the huge amount of generated data, the use of Cloud as new storage means. All these reasons increase the risk of cyber attacks in industry.

Fortunately, there are several solutions to secure the industry and its equipment. Among these mechanisms, we mention firewalls, anti-virus, auditing processes and IDS. Each of these securing mechanisms has a specific role such as detecting and removing malware, preventing unauthorized access or detecting intrusions by IDS. These latter give visibility to the system’s activities, which allows a timely detection and response to any suspicious events [3], [4].

Conclusions

     BIANO-IDS is a new intrusion detection approach combining two kinds of IDS: anomaly-based IDS and specification-based IDS. The approach has been tested on a real environment and the detection accuracy rate is high in addition to the different performance metrics. However, to further improve performance metrics, we intend to explore other traces such as system logs and apply reduction or selection features methods in the future to improve the computation and training time of neural networks.

Abstract

Convolutional neural networks (CNNs) are often used in tasks involving vision processing, and unclear images can hinder the performance of convolutional neural networks and increase its computational time. Furthermore, artificial intelligence (AI) and machine learning (ML) are related technologies, which are considered a branch of computer science, which are used to simulate and enhance human intelligence. In e-healthcare, AI and ML can be used to optimize the workflow, automatically process large amounts of medical data, and provide effective medical decision support. In this paper, the authors take several mainstream artificial intelligence models currently open on the market for reference. In this paper, the optimized model (AL-CNN) is tested for noise image recognition, and the AL-CNN model is established by using activation functions, matrix operations, and feature recognition methods, and the noisy images are processed after custom configuration. Not only does this model require no prior preparation when processing images, but it also improves the accuracy of dealing with noise in convolutional neural networks. In the AL-CNN in this paper, the architecture of the convolutional neural network includes a noise layer and a layer that can be automatically resized. After the comparison of the recognition experiments, the accuracy rate of AL-CNN is 20% higher than that of MatConvNet-moderate, and the accuracy rate is 40% higher than that of MatConvNet-chronic. In the second set of experiments, the accuracy exceeds MXNet and TensorFlow by 50% and 70%, respectively. In addition, the authors optimized the convolutional layer, pooling layer, and loss function of AL-CNN in different parameters, which improved the stability of noise processing, respectively. After customizing the two configuration optimizations, the authors found that the second optimized AL-CNN has higher recognition accuracy, and after the optimization test, the error rate can be continuously decreased as the number of recognition increases in a very short number of times.

Introduction

Convolutional neural network (CNN) is a kind of artificial neural network, unlike recurrent neural, Boltzmann machine, etc., because the visual system is stimulated by neural mechanisms, so the biological model of convolutional neural network can recognize two-dimensional shapes. Convolutional neural network systems can perform convolution operations. Because of its uniqueness, it has excellent performance in many aspects of life, such as image classification, retrieval, and computer-related visual tasks. With more and more research on convolutional neural networks today, this technology will be applied to road engineering, medical imaging, and artificial intelligence involving vision in the future. Also, it achieves better performance than traditional technology.

In the 21st century, artificial intelligence is constantly developing, but at present there is no intelligent model in the true sense that can possess the computing and recognition ability of the traditional neural network system of animals. The concept of deep learning is proposed in this context. Unlike traditional machine learning models, deep learning will complete learning tasks through feature learning and the participation of feature abstraction. The working principle behind the convolutional neural network is deep learning, and because of its excellent processing effect and accuracy, it has played an unprecedented role in many fields of production and life. It mainly includes printing and publishing, logistics and transportation, medical management, and other fields. In the case that there are so many precedents for convolutional neural networks to create excellent results in many fields, this article will demonstrate an intelligent model building and optimization process based on convolutional neural networks. This process has practical experience for practical operators or field beginners to construct their own program models. Moreover, in the field of e-healthcare, applications of AI and machine learning can be widely used in a variety of different scenarios. For example, they can be used to identify a patient’s condition and provide personalized treatment advice. In addition, AI and ML can be used to track a patient’s condition and to remind doctors to examine or adjust treatment options.

Conclusion

In this paper, the authors established an artificial intelligence model AL-CNN based on convolutional neural network and tested its computational efficiency when processing images with different noise points. When AL-CNN processes low-quality images, it does not need any external tools, does not occupy memory, and has fast computing speed. The authors designed a variety of different types of noise to simulate the processing effect of the experiment and compared the model with MatConvNet-moderate, MatConvNet-chronic, MXNet, and TensorFlow. The results show that the model is more efficient than other types in processing images. In order to make this model more stable, the authors made a custom optimization, adding a noise layer and an automatic adjustment tool to the convolutional neural network. At the same time, considering the operational efficiency of different components in AL-CNN, this paper also performs corresponding optimizations for different noise problems. In order to improve the processing performance of CNN, this paper also designs an automatic adjustment noise reduction algorithm based on convolutional neural network in response to different situations. Experimental results show that the optimized AL-CNN has better efficiency in noisy image processing compared with MatConvNet-moderate, MatConvNet-chronic, MXNet, and TensorFlow. Also, because the model does not require prior preparation or other tools, it is faster to process. Finally, AI and ML can be used to automatically identify medical images to improve diagnostic accuracy. They can also be used to analyze patient medical records and data, predict future disease trends, and provide more accurate treatment advice to physicians. Overall, AI and ML in e-healthcare contribute to improve efficiency, provide better care, and lower healthcare costs.

Abstract

     Concrete is one of the most widely used materials in various civil engineering applications. Its global production rate is increasing to meet demand. Mechanical properties of concrete are among important parameters in designing and evaluating its performance. Over the past few decades, machine learning has been used to model real-world problems. Machine learning, as a branch of artificial intelligence, is gaining popularity in many scientific fields such as robotics, statistics, bioinformatics, computer science, and construction materials. Machine learning has many advantages over statistical and experimental models, such as optimal accuracy, high-performance speed, responsiveness in complex environments, and economic cost-effectiveness. Recently, more researchers are looking into deep learning, which is a group of machine learning algorithms, as a powerful method in matters of diagnosis and classification. Hence, this paper provides a review of successful ML and DL model applications to predict concrete mechanical properties. Several modeling algorithms were reviewed highlighting their applications, performance, current knowledge gaps, and suggestions for future research. This paper will assist construction material engineers and researchers in selecting suitable and accurate techniques that fit their applications.

Introduction

     Concrete is the most widely used building material worldwide. With population growth and urbanization, the demand for concrete is expected to reach 18 billion by 2050 [[1], [2], [3]]. In order to improve the design of concrete structures, it is necessary to gain a better understanding of concrete performance, relying on accurate evaluation of its mechanical properties. Among the various properties of concrete, compressive strength has been considered a direct indicator of performance. It is directly related to the safety and performance of the structure throughout its life cycle [1,4,5].

     Concrete is a complex system of combinations of different components (coarse and fine aggregates, water, cement, and additional mixtures) that are randomly distributed throughout the concrete matrix [[6], [7], [8], [9]]. This heterogeneous feature makes it difficult to accurately predict certain mechanical properties, especially compressive strength [[10], [11], [12]]. The most direct way to evaluate the compressive strength of concrete is through physical tests performed on specimens cured to the desired age [13,14]. Such a method for evaluating the compressive strength needs time while being affected by other factors related to specimen fabrications and test operators. Moreover, the test tends to damage the specimens. Empirical regression methods were therefore proposed to predict compressive strength [[15], [16], [17]], but the disadvantage of this method is the non-linear relationship between the concrete mixture and the concrete’s compressive strength. This prevents an accurate regression expression. Numerical simulation is another method that can predict the behavior of concrete. However, a good prediction of concrete behavior is not easy to achieve due to the non-linearity and randomness [[18], [19], [20]].

Conclusions and recommendations

     The present study investigated the performance of different ML/DL models to predict the mechanical properties of concrete. Among the wide range of ML/DL models, support vector machines, decision trees, evolutionary algorithms, and artificial neural networks were examined due to the popularity of these models as well as their frequent use in the field of civil engineering. Therefore, the performance of these models in various studies to evaluate the compressive strength, tensile strength, shear strength, flexural strength, and elastic modulus was evaluated. A comparison of experimental models with ML/DL methods shows that ML/DL models with better updating capability and the ability to analyze large datasets perform better. On the other hand, statistical models are not good for predicting complex structures due to their high cost and time-consuming nature. According to ML/DL methods, selecting the appropriate model for predicting the target strength of concrete should be made by considering different criteria. A review of various studies shows that the relationship between concrete components and mechanical strength is influential in choosing the forecasting model. Therefore, models that can respond in nonlinear space should be used if the relationship is nonlinear. In these cases, SVM and ANN models can be used for their acceptable performance in a non-linear environment with fewer errors. To achieve more accurate results, optimization of these models with metaheuristic algorithms can also be used. But if we need the transparency of the model and the explicit mathematical formula between input and output, we can use decision tree models and evolutionary algorithms. On the other hand, the use of ensemble models to optimize these models, although it results in higher accuracy, increases computation time and model complexity. The combined SVM and ANN models, although they increase the computation time, have accurate results in the face of extensive data. Therefore, based on the study, the best option for predicting the strength of concrete in terms of model accuracy and model implementation is the combined use of SVM and ANN combined models.

Abstract

1. Introduction

2. Literature review

3. Integrated analytical framework for sustainability analysis of supply chain management

4. Results

5. Conclusions

Disclosure statement

References

Abstract

     With the increasing importance of air pollutant emissions to the platform economy and green supply chain management, it is essential to analyse the trend and correlation between particulate matter emissions and supply chain statistics. Typical approaches do not integrate particulate matter prediction with the sustainability analysis, and suffer from common issues such as low classification accuracy and unstable prediction performance. In this study, we propose an integrated analytical framework for sustainability analysis of supply chain management through particulate matter emissions prediction. Specifically, we performance trend and correlation analysis between particulate matter emissions (PM2.5 and PM10) and supply chain statistics in Beijing of China. We combine the boosting algorithm and neural network method to predict particulate matter emissions. Experimental results show that our prediction model achieved high performance. Sustainability analysis shows that the steady growth of the supply chain operations is accompanied by decreasing air pollutant emissions in China.

Introduction

     Today, human society is facing very severe environmental and resource problems. The green and sustainable supply chain are the concrete manifestations of sustainable development strategies that comprehensively consider these two issues in platform economy management. In recent years, continued urbanisation and industrialisation, particularly in developing countries, has led to severe deterioration in air quality and a rapid increase in the degree of contamination (Shi and Yu 2020). Air pollution has substantially affected the living environment of the human population and endangered health (Stieb et al. 2019). The United Nations (UN) issued Sustainable Development Goals (SDGs) in 2015, serving as a blueprint for building a more sustainable future across the globe. There are totally 17 goals in SDGs, addressing issues in economic, environmental, and social areas. These goals are interrelated and expected to be fulfilled by 2030 (2015). PM2.5 emissions have an adverse effect on public health (Yin, Pizzol, and Xu 2017). At present, many countries attach great importance to inhalable total suspended particulates that cannot be blocked by the human upper respiratory tract, especially inhalable aerosol particles with sizes less than 2.5 μm. It is necessary to arouse the stakeholder

Conclusions

     In this paper, an integrated analytical framework targeting sustainability analysis of supply chain management through particulate matter emissions prediction has been proposed. Specifically, we firstly collect and preprocess the particulate matter emission data and supply chain statistics in Beijing of China, then we conduct correlation analysis between air pollutant concentrations and supply chain statistics to uncover the intrinsic associations. Secondly, we combine the boosting ensemble meta-algorithm and neural network method for particulate matter concentration forecasting. The boosting algorithm is used to train a set of individual subnets to improve the efficiency of prediction, and afterwards, the output results of individual subnets are weighted for acquiring the final prediction results. Finally, we perform an analysis on sustainability between air pollutant emissions and supply chain statistics after obtaining the particulate matter prediction results.

     This analytical framework is able to perform sustainability analysis with accuracy and provide reliable insights for the legislation and implementation of green supply chain management. The forecasting of particulate matter emissions conveys basis and justifications for policy-makers to enforce the environmental protection measures associated with air pollution and design efficient policies with the aim of improving the air pollutant management of the supply chain, which will further lead to an improved public health environment, especially in economically developed areas. Because this study mainly targets the prediction of PM2.5 and PM10 concentrations, it cannot comprehensively reveal the geographical distribution of air pollution. There are various factors that affect air quality, and a regression model can be established to predict PM2.5 and PM10 distributions more comprehensively and to determine the key influential factors, which would be important for formulating economic development strategies and implementing environmental protection mechanisms.

Abstract

Introduction

Development of Cloud Computing Platform Based on Neural Network

Experimental Analysi

Conclusion

Data Availability

Conflicts of Interest

References

Abstract

     Aiming at the problems of small data storage, small platform throughput, and high energy consumption in the development of existing cloud computing platforms, this paper develops a cloud computing platform based on neural networks. In the design of the platform, firstly, the functions of the cloud computing platform are determined. Based on the function design, the hardware and software of the cloud computing platform are designed. In the hardware design, the topology of cloud computing platform, data acquisition module, single-chip microcomputer, node deployment, and other types of functional hardware are designed. In the software design, the neural network is mainly used to remove the redundancy of data stored in a cloud computing platform, design the data processing flow of cloud computing platform, and complete the development of cloud computing platform based on neural network. The experimental results show that the cloud computing platform based on the neural network designed in this paper runs faster, the throughput of platform data has been significantly improved, and the operating energy consumption of the platform is low.

Introduction

     With the development of information technology, the Internet has become an indispensable part of people’s life. People cannot get information, publish information, and exchange information without the Internet. In this context, the computing that consumes the most CPU is gradually transferred to the processing of information [1]. The computing power that a single processor can provide is close to the limit. This makes major manufacturers face great challenges; they need to mine useful information from TB and even Pb level data and process this information quickly and efficiently [2].

     Cloud computing technology is a business model rising in recent years [3]. It combines the characteristics of distributed computing, parallel computing, and grid computing and uses the computing cluster built by a large number of ordinary servers and the storage cluster built by a large number of low-cost devices to provide users with scalable computing resources and storage space. Cloud computing technology creates a new application mode, which makes the computing power circulate through the network and provide computing power services comparable to supercomputers through combination, and gradually develops into a network application trend. The deployment and application of supercomputer applications face a very high threshold due to the very expensive hardware investment, while cloud computing combines ordinary personal computers and standard servers into computer clusters through the Internet, becoming a low-cost solution for supercomputing services [4]. In the cloud computing environment, both the number of users and the number of resources are very large. Without a rigorous regulatory mechanism, it is easy to be chaotic and out of order. In addition, China’s laws and regulations on intellectual property are not perfect, piracy is very serious, and the intellectual property rights of resource builders are vulnerable to infringement. In order to ensure the smooth progress of resource sharing in the cloud environment, it is necessary to establish a rigorous regulatory mechanism to supervise the sharing behavior of users. Establishing a professional organization with organizational ability and supervision is a better solution [5].

Results and analyses

     This study develops a cloud computing platform based on neural networks to overcome the challenges of tiny data storage, low platform throughput, and high energy consumption in the development of existing cloud computing platforms. The functions of the cloud computing platform are first identified during platform design. The hardware and software of the cloud computing platform are created based on the function design. The structure of the cloud computing platform, data acquisition module, single-chip microcomputer, node deployment, and other types of functional hardware are designed in the hardware design phase. The neural network is mostly used in software design to reduce redundancy from data stored in a cloud computing platform, design the cloud computing platform’s data processing flow, and finish the construction of a cloud computing platform based on a neural network.

Abstract

1. Introduction

2. Related work

3. Phases of filtered deep learning model for intrusion detection

4. Result and discussion

5. Conclusion

Declaration of Competing Interest

References

Abstract

     Distributed Sensor Networks play a vital role in the day-to-day world of computing applications, from the cloud to the Internet of Things (IoT). These computing applications devices are normally attached with the microcontrollers like Sensors, actuators, and Adriano network connectivity. Defensive network with an Intrusion Detection System thus serves as the need of modern networks. Despite decades of inevitable development, the Intrusion Detection System is still a challenging research area as the existing Intrusion Detection System operates using signature-based techniques rather than anomaly detection. The existing Intrusion Detection System are thus facing challenges for improvement in Intrusion Detection, Handling heterogeneous data sources is hard for discovering zero-day attacks in IoT networks. This paper presents Filtered Deep Learning Model for Intrusion Detection with a Data Communication approach. The proposed model is composed of five phases: Initialization of Sensor Networks, Cluster Formation in addition to Cluster Head Selection, Connectivity, Attack Detection, and Data Broker. The proposed Model for Intrusion Detection was found to outperform the existing Deep Learning Neural Network and Artificial Neural Network. Experimental results showed a better result of 96.12% accuracy than the dominant algorithms.

Introduction

     In current years, cloud computing (CC) is getting increasingly well-liked in educational along with commercial places [1], in addition, its function is also largely accepted for data storage along with retrieval on the cloud environment [2]. For the basis of increasing the number of data on the Cloud servers(CS), the cloud storage consent to store data in numerous inaccessible sites typically hold by “top” business and running their owner solutions, e.g., Google Drive, Dropbox, Amazon Simple Cloud Storages Service (S3) [3] that means the data is amassed distributive. Even if numerous attractive advances to data gathering were developed in the past decade, it still stays as a research focus in full sway with several significant challenges. Certainly, the incessant decrease in sensor size along with cost, the diversity of sensors present on the marketplace together with the incredible progress on Sensor Node communication technologies have expanded potentially the effect of IoT [4].

     IoT comprises SN that is able to compute, sense together with communicate the data by particular communication [5]. For sensing, the SN are utilized, in addition, for computation, numerous algorithms are utilized, at last, for the communication, disparate communication protocols are utilized, for instance, Building Automations and Control Network (BACnet), Distributed Network Protocols version 3 (DNP3) [6], Constrained Application Protocol (CoAP) [7], Message Queue Telemetry Transports (MQTT) [8], together with XMPP (Extensible Messaging and Presences Protocol), AQMP, et cetera, [9]. By utilizing these protocols, the data is amassed.

Conclusion

     DC and effective cloud storage is a challenging one. Many existing works are putting their effort to give a better system. This paper presented a DC and effective distributed cloud storage system centered on the IDS using the FDLNN algorithm. For IDS, the proposed system uses the FDLNN algorithm, and for the data broker, uses the LFEHO algorithm, and for DC, the MQTT and AQMP connectivity are used. The MQTT is best for lightweight devices, and the AQMP is best for the heavyweight devices. For the IDS analysis, the TON_IoT datasets is taken and the performance of the FDLNN is compared with the existing DLNN and ANN algorithms based on the precision, recall, F-Measure, and accuracy. Then, the performance of the data broker is analyzed, for which the proposed LFEHO is compared with the existing IANFIS and ANFIS algorithm based on average turnaround time, throughput, process time, response time as well as AWT. The IDS performance is analyzed based on SNs count and the data broker is analyzed grounded on the number of data. For 500 nodes, the FDLNN has 96.12% accuracy. In data broker analysis for all data count, the proposed LFEHO attained better performance. Hence, the proposed system is found to attain excellent performance on considering the existing algorithms.

Abstract

     The aim of this paper is to develop an algorithm to enhance speech recognition of a stuttered speech. Stuttering is a disorder that affects the fluency of speech by involuntary repetition, prolongation of words/syllables, or involuntary silent intervals. Current speech recognition systems fail to recognize stuttered speech. Methods to detect stutter have been reported in literature but efficient techniques for stutter correction have not been reported. This paper addresses this issue and proposes methods to detect and correct stutter within acceptable time limits. To remove prolongation(s) from the sample, amplitude thresholding through neural networks is developed. Repetitions are removed through string repetition removal algorithm using an existing Text-to-Speech (TTS) system. Thus, the output signal, void of all stutters, produces better speech recognition.

Introduction

     Stuttering is a speech disorder characterized by repetition of sounds, syllables, or words; prolongation of sounds. An individual who faces this disorder knows what he or she intend to say but is unable to produce fluent speech. Millions of people, in today’s world, suffer from various speech disorders like stuttering, lisp, and articulation disorder. This often renders them unable to utilize certain things that a normal person takes for granted, like speech recognition systems.

     Stuttering disorder is characterized by disruptions in the production of speech sounds, called disfluencies. Disfluencies are not necessarily a problem; however, they can hinder communication when a person produces too many of them. Most people often produce brief disfluencies. For instance, some words are repeated or prolonged while others are preceded by an ‘um’ or ‘uh’. In most cases, stuttering has an impact at least on some daily activities. The specific everyday activities that a person finds challenging to perform, vary across individuals. For example, for some people, communication difficulties happen only during specific activities, like talking on the phone, talking before large groups, utilizing everyday tools that use speech as inputs. An author claims, “Stuttering cannot be permanently cured; it may go into remission for a time, or clients can learn to shape their speech into fluent speech with the appropriate speech pathology treatment” [1].

     The main objective of this paper is to present an algorithm that efficiently detects and corrects stutter in a speech segment of a person with stuttering speech disability. The proposed algorithm gives an accuracy level of 86% for 50 stutter speech samples. Two algorithms were used for more precise stutter removal system that can be built on any device.

     The developed system can be incorporated into any existing speech recognition system. It can also serve as a speech therapy system where a user suffering from stutter can sound like the correct output obtained from the system. Hence the device can be used by people suffering from stutter to use the existing virtual assistant services, or talk to others with confidence using the device. This would enhance the level of communication amongst people with this disorder.

Abstract

1. Introduction

2. Logistic regression

3. Neural network regressions and early stopping

4. Global bias regularisation

5. Example

6. Conclusions

Notes on contributor

References

Abstract

     In estimation and prediction theory, considerable attention is paid to the question of having unbiased estimators on a global population level. Recent developments in neural network modelling have mainly focused on accuracy on a granular sample level, and the question of unbiasedness on the population level has almost completely been neglected by that community. We discuss this question within neural network regression models, and we provide methods of receiving unbiased estimators for these models on the global population level.

Introduction

     In recent years, neural networks have become state-of-the-art in all kinds of classification and regression problems. Snapshots of their history and their success are illustrated in LeCun et al. (2015) and Schmidhuber (2015). Their popularity is largely based on the facts that they offer much more modelling flexibility than classical statistical regression models (such as generalised linear models) and that increasing computational power combined with effective training methods have become available, see Rumelhart et al. (1986). Neural networks outperform many other classical statistical approaches in terms of predictive performance on an individual sample level, they allow to include unstructured data such as texts into the regression models, see Lee et al. (2020) for a word embedding example, and they allow for solving rather unconventional regression problems, see Cheng et al. (2020) and Gabrielli (2020) for examples. Therefore, our community has gradually been shifting from a data modelling culture to an algorithmic modelling culture, we refer the reader to Breiman (2001) and Shmueli(2010).

Conclusions

     We have discussed the important problem of considering statistical models that provide unbiased mean estimates on a global population level (balance property). Classical statistical regression models like generalised linear model naturally have this balance property under the canonical link choice because the maximum likelihood estimator provides a critical value of the corresponding optimisation problem. In general, early stop gradient-descent calibrated neural networks fail to have the balance property, because early stopping prevents these models from taking parameters in critical points of the (deviance) loss function. In many applications, this does not reflect a favourable model calibration because it may lead to substantial price misspecification on a global population level. Therefore, we have proposed improvements that lead to globally unbiased solutions. These solutions include an additional generalised linear model optimisation step or shrinkage regularisation to empirical averages. The numerical example shows that we prefer the additional generalized linear model optimisation step.

Abstract

Supplementary Material

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Abstract

Understanding the function learned by a neural network is crucial in many domains, e.g., to detect a model’s adaption to concept drift in online learning. Existing global surrogate model approaches generate explanations by maximizing the fidelity between the neural network and a surrogate model on a sample-basis, which can be very time-consuming. Therefore, these approaches are not applicable in scenarios where timely or frequent explanations are required. In this paper, we introduce a real-time approach for generating a symbolic representation of the function learned by a neural network. Our idea is to generate explanations via another neural network (called the Interpretation Network, or I-Net), which maps network parameters to a symbolic representation of the network function. We show that the training of an I-Net for a family of functions can be performed up-front and subsequent generation of an explanation only requires querying the I-Net once, which is computationally very efficient and does not require training data. We empirically evaluate our approach for the case of low-order polynomials as explanations, and show that it achieves competitive results for various data and function complexities. To the best of our knowledge, this is the first approach that attempts to learn mapping from neural networks to symbolic representations.

1. Introduction

The ability of artificial neural networks to act as general function approximators has led to impressive results in many application areas. However, the price for this universal applicability is the limited interpretability of the trained model. Overcoming this limitation is a subject of active research in the machine learning community [1]. Popular approaches for explaining the results of neural networks such as LIME [2], SHAP [3], or LRP [4] focus on the impact of different attributes on the predictions of the model for certain examples. While this provides a partial explanation for individual examples, it does not shed a light on the complete network function. Especially when dealing with streaming data, uncovering the network function is very important, e.g., for detecting the adjustment of a model to concept drift or for the identification of catastrophic forgetting.

While there are existing approaches for constructing a compact representation of the network function (such as symbolic metamodeling [5] and symbolic regression [6], for instance), they generate their explanations on a sample-basis. Generating explanations through maximizing the fidelity to the neural network on a sample-basis means that the optimization process for finding a suitable explanation must be performed independently for each model we want to interpret. Since this optimization process is usually very time-consuming, it precludes the application of this method in scenarios where timely explanations are required. Furthermore, they require access to the training data, or at least knowledge of its distribution [7]

Abstract

Keywords

1. Introduction

2. Related work

3. Evaluation system of urban green space landscape planning based on PSO-BP neural network model

3.1. Application feasibility and evaluation index of neural network in urban green space landscape planning scheme evaluation

3.2. Network construction of urban green space landscape scheme evaluation system

3.3. Network optimization of urban green space landscape scheme evaluation system

4. Training results and simulation results of urban green space landscape scheme evaluation system

4.1. Training results of PSO-BP neural network system for urban green space landscape scheme evaluation

4.2. Evaluation and analysis of PSO-BP neural network system for urban green space landscape scheme evaluation

5. Conclusion

Funding Statement

Declaration of Competing Interest

References

Abstract

The evaluation simulation of urban green space landscape planning scheme based on PSO-BP neural network model is carried out in this paper. PSO-BP neural network can combine the principle of landscape ecology, integrate more evaluation indicators of ecology and urban development into the urban green space landscape planning scheme, and simply understand and predict human behavior, so as to make a more comprehensive evaluation and prediction of the urban green space landscape planning scheme. It not only has superior memory storage and learning ability, but also can simply understand and predict human behavior, so that more influencing factors that cannot be added in the past can be considered in the scheme evaluation and analysis, and the evaluation of urban green space landscape planning scheme is more comprehensive, scientific and reasonable. Experiments show that PSO-BP neural network has smaller error and better generalization ability than BP neural network. PSO-BP neural network rating model can analyze its more reasonable proportion according to the relationship between different types of green space and indicators, and give corresponding adjustment suggestions, which has guiding significance for the modification and adjustment of urban green space landscape planning scheme.

1. Introduction

With the continuous advancement of urbanization in China, more and more cities ignore the planning and development of urban landscape and the impact of urban green space landscape on the living standard and quality of urban residents,

With the continuous improvement of China’s comprehensive strength and international status, the degree of urbanization in China has been greatly improved, and more and more urban construction is close to fashion cities. But at the same time, many cities ignore the planning and development of urban green space landscape and the impact of urban green space landscape on the living standards and quality of urban residents, resulting in the continuous reduction or even lack of urban green space landscape area, the simplification of urban landscape and the imbalance of ecological environment, Finally, it leads to the contradiction between the urban environment and the quality of life of urban residents [1], [2]. Urban green space landscape planning has also become an important part of urban planning and development, and is an issue of increasing concern to urban residents [3]. With the improvement of China’s economic level, the material life of urban residents has been basically met, the urban living environment has gradually become a problem of increasing concern to urban residents, and the urban green space landscape planning has also become an important part of urban planning and development [3].

Abstract

     This paper presents an Artificial Nerual Network (ANN) for identification of postmenopausal women who are at high risk for developing osteopathy. While 800 patients took part in the study, 180 were used for network training. The following parameters were used: T-score (from −2,5 to −4), Age, Blood calcium level (<1,9 mmol/L), Blood vitamin D level (<20 ng/ml), Hip fracture, Spine fracture, Joint fracture, Glucocorticoids use, Smoking status, and BMI. The network has 10 input parameters and 1 output parameter. For the final architecture of expert system, a neural network with 20 neurons in hidden layer was chosen based on the training results. The signal from each neuron from hidden layer is directed to neuron in output layer, where this neuron processes the signal and gives desired output of the network. The sensitivity was 97,5%, specificity 70%, and accuracy 94,44%.

Introduction

     Osteoporosis is a skeletal illness that causes weakening of the bones, which can lead to increased fractures. People with osteoporosis have decreased bone mass and microarchitectural degeneration of bone tissue, in addition to lower bone strength [1-3].

     Osteoporosis is a skeletal illness that causes weakening of the bones, which can lead to increased fractures. People with osteoporosis have decreased bone mass and microarchitectural degeneration of bone tissue, in addition to lower bone strength [1-3].

     According to data from 2010, 6,6% of men and 22,1% of women over 50 in the EU have osteoporosis. Because of the expanding number of patients worldwide, it is now referred to as a “silent epidemic.” Variable and non-variable factors that raise the chance of developing osteoporosis and bone fractures can be separated. One of the most important variables is smoking, which is linked to decreased bone resistance to mechanical stresses and friction. The effects of excessive alcohol use on bone homeostasis are significant. Caffeine, glucocorticoid therapy, insufficient calcium and vitamin D intake, insufficient physical activity, low BMI, past bone fractures, and a family history of osteoporosis are all risk factors [4-6].

Conclusion

     As osteoporosis in postmenopausal women is a rising problem in the modern world, predictive modelling of the risk of developing osteoprosis is highly desirable. This paper presents the development and validation of an ANN based model for prediction and automated diagnosis of osteoporosis in post-menopausal women based on risk-contributing factors. Giving the high accuracy and sensitivity of proposed ANN for identification of high risk of osteoporosis development in postmenopausal women, it can be concluded that AI has a high potential for decission making for this specific purpose. Prediction of high risk for osteoprosis development can contribute to adjustments in lifestyle and possible prevention of osteoporosis. In addition to the benefit this would have to each individual, the cost reduction in terms of preventing costly interventions necessary in case of osteoporosis development is a significant contribution.

Abstract

     This paper leverages natural language processing, spatial analysis, and statistical analysis to examine the relationship between restaurants’ safety violations and COVID-19 cases. We used location-based consumers’ complaints data during the early stage of business reopening in Florida, USA. First, statistical analysis was conducted to examine the correlation between restaurants’ safety violations and COVID-19 transmission. Second, a neural network-based deep learning model was developed to perform topic modeling based on consumers’ complaints. Third, spatial modeling of the complaints’ geographic distributions was performed to identify the hotspots of consumers’ complaints and COVID-19 cases. The results reveal a positive relationship between consumers’ complaints about restaurants’ safety violations and COVID-19 cases. In particular, consumers’ complaints about personal protection measures had the highest correlation with COVID-19 cases, followed by environmental safety measures. Our analytical methods and findings shed light on customers’ behavioral shifts and hospitality businesses’ adaptive practices during a pandemic.

Introduction

     The outbreak of COVID-19 has significantly impacted the hospitality industry. Business closures, lockdowns, businesses’ capacity control, and travel restrictions have caused an immediate downturn in the hospitality market with potential long-term consequences (Baum and Hai, 2020, Huang et al., 2020). In the United States, most states mandated stay-at-home orders during the first week of April 2020 and allowed businesses to reopen in late April or early May 2020. Immediately after reopening, May and June of 2020 witnessed an upsurge in COVID-19 cases (New York Times, 2020). It was found that 25% of the COVID-19 cases were linked to visits to bars and restaurants (Foster & Mundell, 2020). The ongoing outbreaks have pressured many states such as Florida to reclose bars and restaurants for another short period to curb the fast transition of the disease (Siemaszko, 2020).

     The restaurant industry is one of the most affected industries amid amid a the COVID-19 pandemic (Huang et al., 2021, Gössling et al., 2020). Fears and worries about catching the COVID-19 disease were amplified in food and beverage establishments due to the high-contact nature (Chen & Eyoun, 2021). As the economy reopened, restaurants needed to redesign their business operations to offer consumers higher safety and security standards using flexible service and payment mechanisms. For example, contactless menus, mobile payment systems, on-site sanitizers, routine sanitization practices, and screening diners were implemented by food and beverage establishments to encourage customers to dine out (Dube et al., 2020). Despite growing research on restaurants’ adaptive strategies in such swiftly changing environments, a general halo of uncertainty still lingers about how to enhance restaurants’ quality of service and consumers’ satisfaction while reducing COVID-19 transmission.

Discussion and conclusion

     This research utilized multiple models and approaches to investigate customers’ complaints about restaurants’ safety violations and their correlation with COVID-19 transmission during the pandemic. Thematic analysis and natural language processing modeling were adopted to assess the types of violations. Of the six types of complaints about restaurants, employees’ personal protection (e.g., wearing masks and gloves) and restaurants’ environmental safety (e.g., social distancing) were customers’ major concerns. Statistical analysis confirmed the positive relationship between complaints about restaurants’ violations and the COVID-19 cases. In addition, spatial analysis was performed to identify the hotspots of COVID-19 cases and restaurants’ safety violations at the county level. The findings were consistent with Lee et al.’s (2019) study, suggesting that higher occurrences of food safety violations can be found at tourist destinations. The findings also confirmed the results from Yang et al. (2020), which showed that fast-food restaurants overall receive fewer complaints regarding safety violations than indoor dining services due to the flexibility of food delivery options.